A multi-variable equation for relationship between limiting void ratios of uniform sands and morphological characteristics of their particles

Chang, Ching S.; Deng, Yibing; Meidani, Mehrashk

doi:10.1016/j.enggeo.2018.02.003

Cited by 37 publications

(12 citation statements)

References 33 publications

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“…The advantage of using the measured values for n L and n f is that it provides an accurate calibration points for the model which in turns result in accurate prediction. In case that measurements for n L and n f are not available, one may use existing empirical relations to predict porosity (or void ratio) of endmembers based on their particle size analysis and morphological characteristics such as roundness [68,69]. Such empirical relations are however developed for uniform sands and cannot be applied to pure fine silt or clay due to the complex packing of the latter.…”

Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

“…Such empirical relations are however developed for uniform sands and cannot be applied to pure fine silt or clay due to the complex packing of the latter. That is, the porosity of endmembers can be predicted using existing empirical relations [68,69] and then used as inputs in the current model when only dealing with binary mixtures composed of graded sands. It is important to mention that such approach can reduce the accuracy of the mixtures prediction due to the error introduced in the predicted n L and n f (percentage error varies between 4.9% and 8.5% [68]).…”

Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

“…That is, the porosity of endmembers can be predicted using existing empirical relations [68,69] and then used as inputs in the current model when only dealing with binary mixtures composed of graded sands. It is important to mention that such approach can reduce the accuracy of the mixtures prediction due to the error introduced in the predicted n L and n f (percentage error varies between 4.9% and 8.5% [68]). Moreover, using empirical relations to find the porosity of endmembers would restricted the model applicability as this is not feasible in the presence of very fine silt or clays.…”

Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

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Unified Packing Model for Improved Prediction of Porosity and Hydraulic Conductivity of Binary Mixed Soils

El-Husseiny

2021

Water

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Binary mixed soils, containing coarse sand particles mixed with variable content of fines (fine sand, silt, or clay) are important for several environmental and engineering applications. The packing state (or porosity) of such sand-fines mixtures controls several important physical properties such as hydraulic conductivity. Therefore, developing an analytical packing model to predict porosity of binary mixed soils, based on properties of pure unmixed sand and fines (endmembers), can contribute to predicting hydraulic conductivity for the mixtures without the need for extensive laboratory measurements. Toward this goal, this study presents a unified packing model for the purpose of predicting the porosity and hydraulic conductivity of binary mixed soils as function of fines fraction. The current model modifies an existing packing model developed for coarse binary mixed soils to achieve three main improvements: (1) being inclusive of wide range of binary mixed soils covering the whole range particle sizes, (2) incorporating the impact of cohesive packing behavior of the fines on binary mixture porosity, and (3) accounting for the impact of clay swelling. The presented model is the first of its kind incorporating the combined impact of all three factors: particle size ratio, fines cohesive packing and swelling, on binary mixtures porosity. The predictions of the modified model are validated using experimental published data for the porosity of sand-fines mixtures from 24 different studies. The model shows significant improvement in predicting porosity compared to existing packing models that frequently underestimate the porosity. By using the predicted porosity as an input in Kozeny–Carman formulation, the absolute mean error in predicting hydraulic conductivity, as function of fines fraction for 16 different binary mixed soils, is reduced by 50% when compared to the use of the previous packing model. The current model provides insights about the endmembers properties (porosity, hydraulic conductivity, and grain size) and fines content required to achieve a certain target desirable porosity and hydraulic conductivity of the mixed soils. This can assist the optimization of soil mixing design for various applications.

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Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

Section: Discussion On Input Parameters and Further Potential Developmentioning

confidence: 99%

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Unified Packing Model for Improved Prediction of Porosity and Hydraulic Conductivity of Binary Mixed Soils

El-Husseiny

2021

Water

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“…The new contribution here is to account for the cohesive packing in the dry binary mixtures which improves the prediction when fines have particle sizes less than 0.15 mm. The estimation of porosity for the pure unmixed end members is not the focus of the current study and the reader is referred to other relevant studies about mono-sized or narrowly graded particles packing (e.g., References [70][71][72][73]).…”

Section: Maximum Packing Density Predictionmentioning

confidence: 99%

Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

El-Husseiny

2020

Applied Sciences

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Binary soil mixture, containing large silica particles (sand) mixed with variable content of very fine silt or clay, is an example of a functionally graded material that is important for several science and engineering applications. Predicting the porosity (or void ratio), which is a fundamental quantity that affects other physical properties, of such material as function of fines (clay or silt) fraction can be significant for sediment research and material design optimization. Existing analytical models for porosity prediction work well for binary mixed soils containing multi-sized non-cohesive particles with no clay, while such models frequently underestimate the porosity of sand-clay mixtures. This study aims to present an analytical model that accurately predicts the porosity of mixed granular materials or soils containing sand and very fine silt or clay (cohesive particles). It is demonstrated that accounting for the cohesive nature of very fine particles, which exists due to the effect of inter-particle forces, is a major missing aspect in existing packing models for mixed soils. Consequently, a previously developed linear packing model is modified so that it accounts for fines cohesive packing in sand-fines mixtures. The model prediction is validated using various experimental published data sets for the porosity of sand-fines mixtures. Improvement in the prediction of permeability and maximum packing dry density when incorporating cohesive packing behavior is discussed. The current model also provides important insights on the conditions under which, the lowest permeability and maximum packing state are expected.

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“…Numerous studies show that particle shape has a significant influence on properties of engineering interest for granular soils (Chang et al, 2018;Cho et al, 2006a;Holubec and D'Appolonia, 1973;Liu and Lehane, 2013;Santamarina and Cho, 2004;Vaid et al, 1985;Yang and Luo, 2015). To incorporate this knowledge into geotechnical practice it is necessary to measure shape.…”

Section: Introductionmentioning

confidence: 99%

Sphericity measures of sand grains

Rorato

Arroyo

Andò

et al. 2019

Engineering Geology

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The sphericity of a grain should measure the similitude of its shape with that of a sphere. Sphericity as shape descriptor is of traditional interest for sedimentology. Now it has gained also interest to facilitate discrete element modelling of granular materials. True sphericity was initially defined by a surface ratio that requires three-dimensional (3D) grain surface measurement. That kind of measurement has been practically impossible until recently and, as a consequence, a number of alternative 3D measures and 2D proxies were proposed. In this work we present results from a study of grain shape based on x-ray tomography of two different sand specimens, containing more than 110.000 particles altogether. Sphericity measures were systematically obtained for all grains. 2D proxy measures were also obtained in samples of oriented and not-oriented grains. It is shown that the 2D proxy best correlated with true sphericity is perimeter sphericity, whereas the traditional Krumbein-Sloss chart proxy is poorly correlated. 2D measures acquired through minor axis projection are more closely related to 3D measures than those acquired using random projections.

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A multi-variable equation for relationship between limiting void ratios of uniform sands and morphological characteristics of their particles

Cited by 37 publications

References 33 publications

Unified Packing Model for Improved Prediction of Porosity and Hydraulic Conductivity of Binary Mixed Soils

Unified Packing Model for Improved Prediction of Porosity and Hydraulic Conductivity of Binary Mixed Soils

Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

Sphericity measures of sand grains

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